Privacy protection in collective feedforward

ABSTRACT

Personal audio systems and methods are disclosed. A personal audio system includes a processor to generate a personal audio stream by processing an ambient audio stream in accordance with an active processing parameter set, a circular buffer memory to store a most recent snippet of the ambient audio stream, and an event detector to detect a trigger event. In response to detection of the trigger event, a controller may extract audio feature data from the most recent snippet of the ambient audio stream and transmit the audio feature data and associated metadata to a knowledgebase remote from the personal audio system.

RELATED APPLICATION INFORMATION

This patent is a continuation-in-part of application Ser. No. 14/952,761, filed Nov. 25, 2015, entitled “Processing Sound Using Collective Feedforward”. This patent is related to application Ser. No. 14/681,843, entitled “Active Acoustic Filter with Location-Based Filter Characteristics,” filed Apr. 8, 2015; and application Ser. No. 14/819,298, entitled “Active Acoustic Filter with Automatic Selection Of Filter Parameters Based on Ambient Sound,” filed Aug. 5, 2015.

NOTICE OF COPYRIGHTS AND TRADE DRESS

A portion of the disclosure of this patent document contains material which is subject to copyright protection. This patent document may show and/or describe matter which is or may become trade dress of the owner. The copyright and trade dress owner has no objection to the facsimile reproduction by anyone of the patent disclosure as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright and trade dress rights whatsoever.

BACKGROUND

Field

This disclosure relates to digital active audio filters for use in a listener's ear to modify ambient sound to suit the listening preferences of the listener.

Description of the Related Art

Humans' perception to sound varies with both frequency and sound pressure level (SPL). For example, humans do not perceive lower and higher frequency sounds as well as they perceive sounds at midrange frequencies between 500 Hz and 6,000 Hz. Further, human hearing is more responsive to sound at high frequencies compared to low frequencies.

There are many situations where a listener may desire attenuation of ambient sound at certain frequencies, while allowing ambient sound at other frequencies to reach their ears. For example, at a concert, concert goers might want to enjoy the music, but also be protected from high levels of mid-range sound frequencies that cause damage to a person's hearing. On an airplane, passengers might wish to block out the roar of the engine, but not conversation. At a sports event, fans might desire to hear the action of the game, but receive protection from the roar of the crowd. At a construction site, a worker may need to hear nearby sounds and voices for safety and to enable the construction to continue, but may wish to protect his or her ears from sudden, loud noises of crashes or large moving equipment. Further, a user may wish to engage in conversation and other activities without being interrupted or impaired by annoyance noises such as sounds of engines or motors, crying babies, and sirens. These are just a few common examples where people wish to hear some, but not all, of the sounds in their environment.

In addition to receiving protection from unpleasant or dangerously loud sound levels, listeners may wish to augment the ambient sound by amplification of certain frequencies, combining ambient sound with a secondary audio feed, equalization (modifying ambient sound by adjusting the relative loudness of various frequencies), noise reduction, addition of white or pink noise to mask annoyances, echo cancellation, and addition of echo or reverberation. For example, at a concert, audience members may wish to attenuate certain frequencies of the music, but amplify other frequencies (e.g., the bass). People listening to music at home may wish to have a more “concert-like” experience by adding reverberation to the ambient sound. At a sports event, fans may wish to attenuate ambient crowd noise, but also receive an audio feed of a sportscaster reporting on the event. Similarly, people at a mall may wish to attenuate the ambient noise, yet receive an audio feed of advertisements targeted to their location. These are just a few examples of peoples' listening preferences.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a sound processing system.

FIG. 2 is block diagram of an active acoustic filter.

FIG. 3 is a block diagram of a personal computing device.

FIG. 4 is a functional block diagram of a portion of a personal audio system.

FIG. 5 is a block diagram of a sound knowledgebase.

FIG. 6 is a flow chart of a method for processing sound using collective feedforward.

FIG. 7 is a flow chart of a method for extracting features.

FIG. 8 is a flow chart of another method for processing sound using collective feedforward.

Throughout this description, elements appearing in figures are assigned three-digit reference designators. An element not described in conjunction with a figure has the same characteristics and function as a previously-described element having the same reference designator.

DETAILED DESCRIPTION Description of Apparatus

Referring now to FIG. 1, a sound processing system 100 includes at least one personal audio system 140 and a sound knowledgebase 150 within a cloud 130. The sound processing system 100 may include a large plurality of personal audio systems. In this context, the term “cloud” means a network and all devices that may be accessed by the personal audio system 140 via the network. The term “device” means an apparatus capable of communicating via a network or other digital communications link. Examples of devices include cellular phones, computing tablets, portable computers, desk-top computers, servers, network storage units, and peripheral devices. The cloud 130 may be a local area network, wide area network, a virtual network, or some other form of network together with all devices connected to the network. The cloud 130 may be or include the Internet. The term “knowledgebase” connotes a device that not only stores data, but also learns and stores other knowledge derived from the data. The sound knowledgebase 150 may include a database and one or more servers. The sound knowledgebase 150 will be described in further detail during the discussion of FIG. 5.

The personal audio system 140 includes left and right active acoustic filters 110L, 110R and a personal computing device 120. While the personal computing device 120 is shown in FIG. 1 as a smart phone, the personal computing device 120 may be a smart phone, a desktop computer, a mobile computer, a tablet computer, or any other computing device that is capable of performing the processes described herein. The personal computing device 120 may include one or more processors and memory configured to execute stored software instructions to perform the processes described herein.

The active acoustic filters 110L, 11OR communicate with the personal computing device 120, such as via a first wireless communications link 112. While only a single first wireless communications link 112 is shown in FIG. 1, each active acoustic filter 110L, 11OR may communicate with the personal computing device 120 via separate wireless communication links. The first wireless communications link 112 may use a limited-range wireless communications protocol such as Bluetooth®, WiFi®, ZigBee®, or some other wireless Personal Area Network (PAN) protocol. Alternatively, there may be a direct connection from the personal computing device 120 to one of the active acoustic filters, and an indirect connection to the other active acoustic filter. The indirect connection may be through the direct connection, wherein the directly connected active acoustic filter acts as a relay to the indirectly connected active acoustic filter.

The personal computing device 120 communicates with the cloud 130, for example, via a second communications link 122. In particular, the personal computing device 120 may communicate with the sound knowledgebase 150 within the cloud 130 via the second communications link 122. The second communications link 122 may be a wired connection or may be a wireless communications link using, for example, the WiFi® wireless communications protocol, a mobile telephone data protocol, or another wireless communications protocol.

Optionally, the acoustic filters 110L, 11OR may communicate directly with the cloud 130 via a third wireless communications link 114. The third wireless communications link 114 may be an alternative to, or in addition to, the first wireless communications link 112. The third wireless connection 114 may use, for example, the WiFi® wireless communications protocol, or another wireless communications protocol. The acoustic filters 110L, 11OR may communicate with each other via a fourth wireless communications link (not shown). This fourth wireless communication link may provide an indirect connection of one active acoustic filter to the cloud 130 through the other active acoustic filter.

FIG. 2 is block diagram of an active acoustic filter 200, which is representative of the active acoustic filter 110L and the active acoustic filter 110R. The active acoustic filter 200 includes at least one microphone 210, a preamplifier 215, an analog-to-digital (A/D) converter 220, a wireless interface 225, a processor 230, a memory 235, a digital-to-analog (D/A) converter 240, an amplifier 245, a speaker 250, and a battery (not shown), all of which may be contained within a housing 290. The active acoustic filter 200 receives ambient sound 205 and outputs personal sound 255. In this context, the term “sound” refers to acoustic waves propagating in air. “Personal sound” means sound that has been processed, modified, or tailored in accordance with a user's personal preferences. The term “audio” refers to an electronic representation of sound, which may be an analog signal or digital data.

The housing 290 is configured to interface with a user's ear by fitting in, on, or over the user's ear such that the ambient sound 205 is mostly excluded from reaching the user's ear canal and the personal sound 255 generated by the active acoustic filter is provided directly into the user's ear canal.

The housing 290 has at least a first aperture 292 for accepting the ambient sound 205 and a second aperture 294 to allow the personal sound 255 to be output into the user's outer ear canal. The housing 290 may have more than one aperture for accepting ambient sound, each of which may be coupled to a separate microphone. The housing 290 may be, for example, an earbud housing. The term “earbud” means an apparatus configured to fit, at least partially, within and be supported by a user's ear. An earbud housing typically has a portion that fits within or against the user's outer ear canal. An earbud housing may have other portions that fit within the concha or pinna of the user's ear.

The microphone 210 converts the ambient sound 205 into an electrical signal that is amplified by preamplifier 215 and converted into an ambient audio stream 222 by A/D converter 220. In this context, the term “stream” means a sequence of digital samples. The “ambient audio stream” is a sequence of digital samples representing the ambient sound received by the active acoustic filter 200. The ambient audio stream 222 is processed by processor 230 to provide a personal audio stream 232. The processing performed by the processor 230 will be discussed in more detail subsequently. The personal audio stream 232 is converted into an analog signal by D/A converter 240. The analog signal output from D/A converter 240 is amplified by amplifier 245 and converted into personal sound 255 by speaker 250.

The microphone 210 may be one or more transducers for converting sound into an electrical signal that is sufficiently compact for use within the housing 290. The preamplifier 215 is configured to amplify the electrical signal output from the microphone 210 to a level compatible with the input of the A/D converter 220. The preamplifier 215 may be integrated into the A/D converter 220, which, in turn, may be integrated with the processor 230. In the situation where the active acoustic filter 200 contains more than one microphone, a separate preamplifier may be provided for each microphone.

The A/D converter 220 digitizes the output from preamplifier 215, which is to say converts the output from preamplifier 215 into a series of digital ambient audio samples at a rate at least twice the highest frequency present in the ambient sound. For example, the A/D converter may output the ambient audio stream 222 in the form of sequential audio samples at rate of 40 kHz or higher. The resolution of the ambient audio stream 222 (i.e., the number of bits in each audio sample) may be sufficient to minimize or avoid audible sampling noise in the processed output sound 255. For example, the A/D converter 220 may output an ambient audio stream 222 having 12 or more bits of amplitude resolution. In the situation where the active acoustic filter 200 contains more than one microphone with respective preamplifiers, the outputs from the preamplifiers may be digitized separately, or the outputs of some or all of the preamplifiers may be combined prior to digitization.

The wireless interface 225 provides digital acoustic filter 200 with a connection to one or more wireless networks 295 using a limited-range wireless communications protocol such as Bluetooth®, WiFi®, ZigBee®, or other wireless personal area network protocol. The wireless interface 225 may be used to receive data such as parameters for use by the processor 230 in processing the ambient audio stream 222 to produce the personal audio stream 232. The wireless interface 225 may be used to receive a secondary audio feed. The wireless interface 225 may be used to export the personal audio stream 232, which is to say transmit the personal audio stream 232 to a device external to the active acoustic filter 200. The external device may then, for example, store and/or publish the personal audio stream, for example via social media.

The processor 230 may include one or more processor devices such as a microcontroller, a microprocessor, and/or a digital signal processor. The processor 230 can include and/or be coupled to the memory 235. The memory 235 may store software programs, which may include an operating system, for execution by the processor 230. The memory 235 may also store data for use by the processor 230. The data stored in the memory 235 may include, for example, digital sound samples and intermediate results of processes performed on the ambient audio stream 222. The data stored in the memory 235 may also include a user's listening preferences, and/or rules and parameters for applying particular processes to convert the ambient audio stream 222 into the personal audio stream 232. The memory 235 may include a combination of read-only memory, flash memory, and static or dynamic random access memory.

The D/A converter 240 converts the personal audio stream 232 from the processor 230 into an analog signal. The processor 230 outputs the personal audio stream 232 as a series of samples typically, but not necessarily, at the same rate as the ambient audio stream 222 is generated by the A/D converter 220. The analog signal output from the D/A converter 240 is amplified by the amplifier 245 and converted into personal sound 255 by the speaker 250. The amplifier 245 may be integrated into the D/A converter 240, which, in turn, may be integrated with the processor 230. The speaker 250 can be any transducer for converting an electrical signal into sound that is suitably sized for use within the housing 290.

A battery or other power supply (not shown) provides power to various elements of the active acoustic filter 200. The battery may be, for example, a zinc-air battery, a lithium ion battery, a lithium polymer battery, a nickel cadmium battery, or a battery using some other technology.

The depiction in FIG. 2 of the active acoustic filter 200 as a set of functional blocks or elements does not imply any corresponding physical separation or demarcation. All or portions of one or more functional elements may be located within a common circuit device or module. Any of the functional elements may be divided between two or more circuit devices or modules. For example, all or portions of the analog-to-digital (A/D) converter 220, the processor 230, the memory 235, the analog signal by digital-to-analog (D/A) converter 240, the amplifier 245, and the wireless interface 225 may be contained within a common signal processor circuit device.

FIG. 3 is a block diagram of an exemplary personal computing device 300, which may be the personal computing device 120. As shown in FIG. 3, the personal computing device 300 includes a processor 310, memory 320, a user interface 330, a communications interface 340, and an audio interface 350. Some of these elements may or might not be present, depending on the implementation. Further, although these elements are shown independently of one another, each may, in some cases, be integrated into another.

The processor 310 may be or include one or more microprocessors, microcontrollers, digital signal processors, application specific integrated circuits (ASICs), or a system-on-a-chip (SOCs). The memory 320 may include a combination of volatile and/or non-volatile memory including read-only memory (ROM), static, dynamic, and/or magnetoresistive random access memory (SRAM, DRM, MRAM, respectively), and nonvolatile writable memory such as flash memory.

The memory 320 may store software programs and routines for execution by the processor. These stored software programs may include an operating system such as the Apple® MacOS or IOS operating systems or the Android® operating system. The operating system may include functions to support the communications interface 340, such as protocol stacks, coding/decoding, compression/decompression, and encryption/decryption. The stored software programs may include an application or “app” to cause the personal computing device to perform portions of the processes and functions described herein.

The user interface 330 may include a display and one or more input devices such as a touch screen.

The communications interface 340 includes at least one interface for wireless communication with external devices. The communications interface 340 may include one or more of a cellular telephone network interface 342, a wireless local area network (LAN) interface 344, and/or a wireless personal area network (PAN) interface 336. The cellular telephone network interface 342 may use one or more cellular data protocols. The wireless LAN interface 344 may use the WiFi® wireless communication protocol or another wireless local area network protocol. The wireless PAN interface 346 may use a limited-range wireless communication protocol such as Bluetooth®, Wi-Fi®, ZigBee®, or some other public or proprietary wireless personal area network protocol. When the personal computing device 300 is deployed as part of a personal audio system, such as the personal audio system 140, the wireless PAN interface 346 may be used to communicate with the active acoustic filter devices 110L, 110R. The cellular telephone network interface 342 and/or the wireless LAN interface 344 may be used to communicate with the cloud 130.

The communications interface 340 may include radio-frequency circuits, analog circuits, digital circuits, one or more antennas, and other hardware, firmware, and software necessary for communicating with external devices. The communications interface 340 may include one or more processors to perform functions such as coding/decoding, compression/decompression, and encryption/decryption as necessary for communicating with external devices using selected communications protocols. The communications interface 340 may rely on the processor 310 to perform some or all of these function in whole or in part.

The audio interface 350 may be configured to both input and output sound. The audio interface 350 may include more or more microphones, preamplifiers and A/D converters that perform similar functions as the microphone 210, preamplifier 215 and A/D converter 220 of the active acoustic filter 200. The audio interface 350 may include more or more D/A converters, amplifiers, and speakers that perform similar functions as the D/A converter 240, amplifier 245 and speaker 250 of the active acoustic filter 200.

The personal computing device 300 may be configured to perform geo-location, which is to say to determine its own location. Geo-location may be performed, for example, using a Global Positioning System (GPS) receiver or by some other method.

FIG. 4 shows a functional block diagram of a portion of an exemplary personal audio system 400, which may be the personal audio system 140. The personal audio system 400 includes one or two active acoustic filters, such as the active acoustic filters 110L, 110R, and a personal computing device, such as the personal computing device 120. The functional blocks shown in FIG. 4 may be implemented in hardware, by software running on one or more processors, or by a combination of hardware and software. The functional blocks shown in FIG. 4 may be implemented within the personal computing device, or within one or both active acoustic filters, or may be distributed between the personal computing device and the active acoustic filters.

The personal audio system 400 includes an audio processor 410, a controller 420, a dataset memory 430, an audio snippet memory 440, a user interface 450 and a geo-locator 460. The audio processor 410 and/or the controller 420 may include their own memory, which is not shown, for storing program instructions, intermediate results, and other data.

The audio processor 410 may be or include one or more microprocessors, microcontrollers, digital signal processors, application specific integrated circuits (ASICs), or a system-on-a-chip (SOCs). The audio processor 410 may be located within an active acoustic filter, within the personal computing device, or may be distributed between a personal computing device and one or two active acoustic filters.

The audio processor 410 receives and processes a digital ambient audio stream, such as the ambient audio stream 222, to provide a personal audio stream, such as the personal audio stream 232. The audio processor 410 may perform processes including filtering, equalization, compression, limiting, and/or other processes. Filtering may include high-pass, low-pass, band-pass, and band-reject filtering. Equalization may include dividing the ambient sound into a plurality of frequency bands and subjecting each of the bands to a respective attenuation or gain. Equalization may be combined with filtering, such as a narrow band-reject filter to suppress a particular objectionable component of the ambient sound. Compression may be used to alter the dynamic range of the ambient sound such that louder sounds are attenuated more than softer sounds. Compression may be combined with filtering or with equalization such that louder frequency bands are attenuated more than softer frequency bands. Limiting may be used to attenuate louder sounds to a predetermined loudness level without attenuating softer sounds. Limiting may be combined with filtering or with equalization such that louder frequency bands are attenuated to a defined level while softer frequency bands are not attenuated or attenuated by a smaller amount.

The audio processor 410 may also add echo or reverberation to the ambient audio stream. The audio processor 410 may also detect and cancel an echo in the ambient audio stream. The audio processor 410 may further perform noise reduction processing.

The audio processor 410 may receive a secondary audio stream. The audio processor 410 may incorporate the secondary audio stream into the personal audio stream. The secondary audio stream may be added to the ambient audio stream before processing, after all processing of the ambient audio stream is performed, or at an intermediate stage in the processing of the ambient audio stream. The secondary audio stream might not be processed, or may be processed in the same manner as or in a different manner than the ambient audio stream.

The audio processor 410 may process the ambient audio stream, and optionally the secondary audio stream, in accordance with an active processing parameter set 425. The active processing parameter set 425 may define the type and degree of one or more processes to be performed on the ambient audio stream and, when desired, the secondary audio stream. The active processing parameter set may include numerical parameters, filter models, software instructions, and other information and data to cause the audio processor to perform desired processes on the ambient audio stream. The extent and format of the information and data within active processing parameter set 425 may vary depending on the type of processing to be performed. For example, the active processing parameter set 425 may define filtering by a low pass filter with a particular cut-off frequency (the frequency at which the filter start to attenuate) and slope (the rate of change of attenuation with frequency) and/or compression using a particular function (e.g. logarithmic). For further example, the active processing parameter set 425 may define the plurality of frequency bands for equalization and provide a respective attenuation or gain for each frequency band. In yet another example, the processing parameters may define a delay time and relative amplitude of an echo to be added to the digitized ambient sound.

The audio processor 410 may receive the active processing parameter set 425 from the controller 420. The controller 420, in turn, may obtain the active processing parameter set 425 from the user via the user interface 450, from the cloud (e.g. from the sound knowledgebase 150 or another device within the cloud), or from the dataset memory 430 within the personal audio system 400.

The dataset memory 430 may store one or more processing parameter sets 432, which may include a copy of the active processing parameter set 425. The dataset memory 430 may store dozens or hundreds or an even larger number of processing parameter sets 432. Each processing parameter set 432 may be associated with at least one indicator, where an “indicator” is data indicating conditions or circumstances where the associated processing parameter set 432 is appropriate for selection as the active processing parameter set 425. The indicators associated with each processing parameter set 432 may include one or more of a location 434, an ambient sound profile 436, and a context 438. The combination of a processing parameter set and its associated indicators is considered a “dataset”.

Locations 434 may be associated with none, some, or all of the processing parameter sets 432 and stored in the dataset memory 430. Each location 434 defines a geographic position or limited geographic area where the associated set of processing parameters 432 is appropriate. A geographic position may be defined, for example, by a street address, longitude and latitude coordinates, GPS coordinates, or in some other manner. A geographic position may include fine-grained information such as a floor or room number in a building. A limited geographic area may be defined, for example, by a center point and a radius, by a pair of coordinates identifying diagonal corners of a rectangular area, by a series of coordinates identifying vertices of a polygon, or in some other manner.

Ambient sound profiles 436 may be associated with none, some, or all of the processing parameter sets 432 and stored in the dataset memory 430. Each ambient sound profile 436 defines features and characteristics of an ambient sound environment in which the associated processing parameter set 432 is appropriate. Each ambient sound profile 436 may define the features and characteristics of the ambient sound environment by a finite number of numerical values. For example, an ambient profile may include numerical values for some or all of an overall loudness level, a normalized or absolute loudness of predetermined frequency bands, a spectral envelope shape, spectrographic features such as rising or falling pitch, frequencies and normalized or absolute loudness levels of dominant narrow-band sounds, an indicator of the presence or absence of odd and/or even harmonics, a normalized or absolute loudness of noise, a low frequency periodicity (e.g. the “beat” when the ambient sound includes music), and numerical values quantifying other features and/or characteristics.

Contexts 438 may be associated with none, some, or all of the processing parameter sets 432 and stored in the dataset memory 430. Each context 438 is a descriptive name of an environment or situation in which the associated processing parameter set 432 is appropriate. Examples of contexts include “airplane cabin,” “subway,” “urban street,” “siren,” and “crying baby.” A context is not necessarily associated with a specific geographic location, but may be associated with a generic location such as, for example, “airplane,” “subway,” and “urban street.” A context may be associated with a type of ambient sound such as, for example, “siren,” “crying baby,” and “rock concert.” A context may be associated with one or more sets of processing parameters. When a context is associated with multiple processing parameter sets 432, selection of a particular processing parameter set may be based on location or ambient sound profile. For example, “siren” may be associated with a first set of processing parameters for locations in the United States and a different set of processing parameters for locations in Europe.

The controller 420 may select a processing parameter set 432 for use as the active processing parameter set 425 based on location, ambient sound profile, context, or a combination thereof. Retrieval of a processing parameter set 432 may be requested by the user via the user interface 450. Alternatively or additionally, retrieval of a processing parameter set 432 may be initiated automatically by the controller 420.

For example, the controller 420 may include a profile developer 422 to analyze the ambient audio stream to develop a current ambient sound profile. In this case, the controller 420 compares the current ambient sound profile with a stored prior ambient sound profile. When the current ambient sound profile is judged, according to first predetermined criteria, to be substantially different from the prior ambient sound profile, the controller 420 initiates retrieval of a new processing parameter set 432.

The personal audio system 400 may contain a geo-locator 460. The geo-locator 460 determines a geographic location of the personal audio system 400 using GPS, cell tower triangulation, or some other method. As described in co-pending application Ser. No. 14/681,843, entitled “Active Acoustic Filter with Location-Based Filter Characteristics,” the controller 420 may compare the geographic location of the personal audio system 400, as determined by the geo-location 460, with location indicators 434 stored in the dataset memory 430. When one of the location indicators 434 matches, according to second predetermined criteria, the geographic location of the personal audio system 400, the associated processing parameter set 432 may be retrieved and provided to the audio processor 410 as the active processing parameter set 425.

As described in co-pending application Ser. No. 14/819,298, entitled “Active Acoustic Filter with Automatic Selection of Filter Parameters Based on Ambient Sound,” the controller may select a processing parameter set 432 based on the ambient sound. The controller 420 may compare the profile of the ambient sound, as determined by the profile developer 422, with profile indicators 436 stored in the dataset memory 430. When one of the profile indicators 436 matches, according to third predetermined criteria, the profile of the ambient sound, the associated processing parameter set 432 may be retrieved and provided to the audio processor 410 as the active processing parameter set 425.

In some circumstances, for example upon user request or when a matching location or profile is not found in the dataset memory 430, the controller 420 may present a list of the contexts 438 on a user interface 450. A user may then manually select one of the listed contexts and the associated processing parameter set 432 may be retrieved and provided to the audio processor 410 as the active processing parameter set 425. For example, assuming the user interface includes a display with a touch screen, the list of contexts may be displayed on the user interface as array of soft buttons. The user may then select one of the contexts by pressing the associated button.

Datasets (i.e., processing parameter sets 432 and associated indicators 434, 436, 438) may be entered into the dataset memory 430 in several ways. Datasets may have been stored in the dataset memory 430 during manufacture of the personal audio system 400. Datasets may have been stored in the dataset memory 430 during installation of an application or “app” on the personal computing device that is a portion of the personal audio system.

Additional datasets stored in the dataset memory 430 may have been created by the user of the personal audio system 400. For example, an application running on the personal computing device may present a graphical user interface through which the user can select and control parameters to edit an existing processing parameter set and/or to create a new processing parameter set. In either case, the edited or new processing parameter set may be saved in the dataset memory 430 in association with one or more of a current ambient sound profile provided by the profile developer 422, a location of the personal audio system 400 provided by the geo-locator 460, and a context or name entered by the user via the user interface 450. The edited or new processing parameter set to be saved in the dataset memory 430 automatically or in response to a specific user command.

Datasets may be developed by third parties and made accessible to the user of the personal audio system 400, for example, via a network.

Further, datasets may be downloaded from the cloud, such as from the sound knowledgebase 150 in the cloud 130, and stored in the dataset memory 430. For example, newly available or revised processing parameter sets 432 and associated indicators 434, 436, 438 may be pushed from the cloud to the personal audio system 400 automatically. Newly available or revised processing parameter sets 432 and associated indicators 434, 436, 438 may be downloaded from the cloud by the personal audio system 400 at periodic intervals. Newly available or revised processing parameter sets 432 and associated indicators 434, 436, 438 may be downloaded from the cloud by the personal audio system 400 in response to a request from a user.

To support development of new and/or revised processing parameter sets, the personal audio system may upload information, such as to the sound knowledgebase 150 in the cloud 130.

The personal audio system may contain an audio snippet memory 440. The audio snippet memory 440 may be, for example, a revolving or circular buffer memory having a fixed size where the newest data overwrites the oldest data such that, at any given instant, the buffer memory contains a predetermined amount of the most recently stored data. The audio snippet memory 440 may store, for example, the most recent audio stream data for a period of 2 seconds, 5 seconds, 10 seconds, 30 seconds, or some other period.

The audio snippet memory 440 may store a “most recent portion” of an audio stream, where the “most recent portion” is the time period immediately preceding the current time. The audio snippet memory 440 may store the most recent portion of the ambient audio stream input to the audio processor 410 (as shown in FIG. 4), in which case the audio snippet memory 440 may be located within one or both of the active acoustic filters of the personal audio system. The audio snippet memory 440 may store the most recent portion of an audio stream derived from the audio interface 350 in the personal computing device of the personal audio system, in which case the audio snippet memory may be located within the personal computing device 120. In either case, the duration of the most recent portion of the audio stream stored in the audio snippet memory 440 may be sufficient to capture very low frequency variations in the ambient sound such as, for example, periodic frequency modulation of a siren or interruptions in a baby's crying when the baby inhales.

The personal audio system my include an event detector 424 to detect trigger events, which is to say events that trigger uploading the content of the audio snippet memory and associated metadata to the remote device. The event detector 424 may be part of, or coupled to, the controller 420. The event detector 424 may detect events that indicate or cause a change in the active processing parameter set 425 used by the audio processor 410 to process the ambient audio stream. Examples of such events detected by the event detector include the user entering commands via the user interface 450 to modify the active processing parameter set 425 or to create a new processing parameter set; the user entering a command via the user interface 450 to save a modified or new processing parameter set in the dataset memory 430; automatic retrieval, based on location or ambient sound profile, of a selected processing parameter set from the dataset memory 430 for use as the active processing parameter set; and user selection, for example from a list or array of buttons presented on the user interface 450, of a selected processing parameter set from the dataset memory 430 for use as the active processing parameter set. Such events may be precipitated, for example, by a change in the ambient sound environment or by user dissatisfaction with the sound of the personal audio stream obtained with the previously-used active processing parameter set.

Application Ser. No. 14/952,761, “Processing Sound Using Collective Feedforward”, describes a personal audio system that, in response to a trigger event, uploads a most recent audio snippet (i.e., the content of an audio snippet memory) and associated metadata to a remote device. Uploading the most recent audio snippet allows the remote device to perform various analyses to determine the characteristics of the ambient sound immediately prior to the event. However, the most recent audio snippet may contain speech of the user of the personal audio system or other persons. Thus uploading the most recent audio snippet may raise privacy concerns.

The personal audio system 400 may include a feature extractor 426 to extract feature data from the most recent audio snippet stored in the audio snippet memory 440. Feature data that may be extracted from the most recent audio snippet include, for example, data such as means (arithmetic, geometric, harmonic), centroid, variance, standard deviation, spectral skew, kurtosis, spectral envelope shape, spectral rolloff, spread, flatness, spectral flux, Mel frequency cepstral coefficients, pitch, tonal power ratio, harmonic-to-average power ratio, maximum of autocorrelation function, zero crossing rate, RMS power, peak power, crest factor, and/or amplitude/power envelope including estimation of attack/decay rates.

Upon detection of an event by the event detector 424, the feature extractor 426 may extract feature data from the most recent audio snippet stored in the audio snippet memory. The type of audio featurization to apply, and the type and amount of feature data extracted from the most recent audio snippet may depend on the characteristics of the stored audio. Some feature data may be extracted from the entire content of the audio snippet memory 440 and other feature data may be extracted from multiple consecutive time slices of the audio data stored in the audio snippet memory 440. The extracted feature data may then be transmitted to a remote device such as the sound knowledge base in the cloud 130. The feature data transmitted to the remote device may be configured to allow analysis of the ambient sound immediately preceding the event, but not allow reconstruction of speech or recognition of the user or other speakers.

Metadata may be transmitted along with the extracted feature data. The transmitted metadata may include a location of the personal audio system 400, which may be provided by the geo-locator 460. When the trigger event was a user-initiated or automatic retrieval of a selected processing parameter set from the parameter memory, the transmitted metadata may include an identifier of the selected processing parameter set and/or the complete selected processing parameter set. When the trigger event was the user modifying a processing parameter set or creating a new processing parameter set, the transmitted metadata may include the modified or new processing parameter set. Further, the user may be prompted or required to enter, such as via the user interface 450, a context, descriptor, or other tag to be associated with the extracted feature data and transmitted. To preserve user privacy, the transmitted metadata may exclude information that identifies the user or the user's device.

FIG. 5 is a functional block diagram of an exemplary sound knowledgebase 500, which may be the sound knowledgebase 150 within the sound processing system 100. The sound knowledgebase 500 includes a processor 510 coupled to a memory/storage 520 and a communications interface 540. These functions may be implemented, for example, in a single server computer or by one or more real or virtual servers within the cloud.

The processor 510 may be or include one or more microprocessors, microcontrollers, digital signal processors, application specific integrated circuits (ASICs), or a system-on-a-chip (SOCs). The memory/storage 520 may include a combination of volatile and/or non-volatile memory. The memory/storage 520 may include one or more storage devices that store data on fixed or removable storage media. The term “storage media” means a physical object adapted for storing data, which excludes transitory media such as propagating signals or waves. Examples of storage media include magnetic discs and optical discs.

The communications interface 540 includes at least one interface for wired or wireless communications with external devices including a plurality of personal audio systems.

The memory/storage 520 may store a database 522 having a plurality of records. Each record in the database 522 may include a set of audio feature data and associated metadata received from one of a plurality of personal audio systems, such as the personal audio system 400, via the communication interface 540. The memory/storage 520 may also store software programs and routines for execution by the processor. These stored software programs may include an operating system. The operating system may include functions to support the communications interface 540, such as protocol stacks, coding/decoding, compression/decompression, and encryption/decryption. The stored software programs may include a database application (also not shown) to manage the database 522.

The stored software programs in the memory/storage 520 may include a feature data analysis application 524 to analyze audio feature data received from the plurality of personal audio systems. The feature data analysis application 524 may, for example, extract or develop additional data representing the characteristics and features of the ambient sound at the personal audio system that provided the audio feature data. Additional data extracted or developed by feature data analysis application 524 may be stored in the database 522 as part of the record containing the corresponding audio feature data and metadata.

The stored software programs may include a parameter set learning application 526 to learn revised and/or new processing parameter sets from the audio feature data, additional data, and metadata stored in the database 522. The parameter set learning application 526 may use a variety of analytical techniques to learn revised and/or new processing parameter sets. These analytical techniques may be applied to numerical and statistical analysis of audio feature data, additional data, and numerical metadata such as location, date, and time metadata. These analytical techniques may include, for further example, semantic analysis of tags, descriptors, contexts, and other non-numerical metadata. Further, the parameter set learning application 526 may use known machine learning techniques such as neural nets, fuzzy logic, adaptive neuro-fuzzy inference systems, or combinations of these and other machine learning methodologies to learn revised and/or new processing parameter sets.

As an example of a learning process that may be performed by the parameter set learning application 526, the records in the database 522 may be sorted into a plurality of clusters based according to audio feature data, location, tag or descriptor or some other factor. Some or all of these clusters may optionally be sorted into sub-clusters based on another factor. When records are sorted into clusters or sub-clusters based on non-numerical metadata (e.g., tags or descriptors) semantic analysis may be used to combine like metadata into a manageable number of clusters or sub-clusters. A consensus processing parameter set may then be developed for each cluster or sub-cluster. For example, clear outliers may be discarded and the consensus processing parameter set may be formed from the medians or means of processing parameters within the remaining processing parameter sets.

The memory/storage 520 may include a master parameter memory 528 to store all processing parameter sets and associated indicators currently used within the sound processing system 100. New or revised processing parameter sets developed by the parameter set learning application 526 may be stored in the master parameter memory 528. Some or all of the processing parameter sets stored in the master parameter memory 528 may be downloaded via the communications interface 540 to each of the plurality of personal audio systems in the sound processing system 100. For example, new or recently revised processing parameter sets may be pushed to some or all of the personal audio systems as available. Processing parameters sets, including new and revised processing parameter sets may be downloaded to some or all of the personal audio systems at periodic intervals. Processing parameters sets, including new and revised processing parameter sets may be downloaded upon request from individual personal audio systems.

Description of Processes

FIG. 6 shows flow charts of methods 600 and 700 for processing sound using collective feedforward. The methods 600 and 700 may be performed by a sound processing system, such as the sound processing system 100 (FIG. 1). The sound processing system may include a large plurality of personal audio systems, each having characteristics as akin to the personal audio system 140 (FIG. 1). The method 700 will, in most cases, produce better results with scale, so having more personal audio systems will usually be better. Thus, having thousands of personal audio systems, or even millions, may produce superior benefits.

The method 600 may be performed by each personal audio system concurrently but not necessarily synchronously. The method 700 may be performed by the sound knowledgebase concurrently with the method 600. All or portions of the methods 600 and 700 may be performed by hardware, by software running on one or more processors, or by a combination of hardware and software. All or portions of the method 600 may be performed by an active acoustic filter, such as the active acoustic filter 200, or may be distributed between an active acoustic filter and a personal computing device, such as the personal computing device 120. Although shown as a series of sequential actions for ease of discussion, the actions from 710 to 750 may occur continuously and simultaneously, and that the actions from 610 to 660 may be performed concurrently by the plurality of personal audio systems. Further, in FIG. 6, process flow is indicated solid arrows and information flow is indicated by dashed arrows.

The method 600 may start at 605 and run continuously until stopped (not shown). At 610, one or more processing parameter sets and associated indicators may be stored in a parameter memory, such as the dataset memory 430, within the personal audio system. Initially, one or more processing parameter sets may be stored in the personal audio system during manufacture or during installation of a personal audio system application on a personal computing device. Subsequently, new and/or revised processing parameter sets and associated indicators may be received from the sound knowledgebase.

At 620, an ambient audio stream derived from ambient sound may be processed in accordance with an active processing parameter set selected from the processing parameters sets stored at 610. Processes that may be performed at 620 were previous described. Concurrently with processing the ambient audio stream at 620, a most recent portion of the ambient audio stream may be stored in a snippet memory at 630, also as previously described.

At 640, a determination may be made whether or not a trigger event has occurred. Trigger events were previously described. When a determination is made at 640 that a trigger event has not occurred (“no” at 640), the processing at 620 and storing at 630 may continue. When a determination is made at 640 that a trigger event has occurred (“yes” at 640), a processing parameter set may be stored or retrieved at 650 as appropriate. At 650, a current processing parameter set, for example, as defined or edited by a user, may be stored in dataset memory 430. At 650, a previously stored processing parameter set may be retrieved from the dataset memory 430, either in repose to a user action or automatically, for example in response to a change in the ambient sound or user location.

At 660, feature data may be extracted from the most recent audio snippet. The audio snippet memory may be located within one or both active acoustic filters, or within a personal computing device, or may be distributed between the active acoustic filters and the personal computing device. Feature extraction may be performed by a processor within one or both active acoustic filters, a processor within the personal computing device, or may be distributed between the active acoustic filters and the personal computing device. Audio snippet data stored in one or both active acoustic filters may be transmitted to the personal computing device prior to feature extraction.

As previously described, data regarding a large number of different features can be extracted from an audio signal. Extracting all of the possible feature data from the most recent audio snippet may present an unreasonable or undesirable burden on the processor(s) within a personal audio system. Further only a subset of the possible feature data may be relevant to any given ambient sound environment.

To reduce the processing burden, an optional process 800, shown in FIG. 7, may be employed at 660 to preselect relevant feature data before some or all of the feature data is extracted. At 810, a preliminary analysis is performed on the most recent audio snippet. Alternately or additionally, at 820, a query may be sent, via a user interface, to a user to identify a sound (e.g. gunshot, breaking glass, automobile crash, etc.) captured in the most recent audio snippet. At 830, a subset of the possible audio feature data is selected as particularly relevant to the most recent audio snippet. The relevant audio feature data may be selected based on the results of the preliminary analysis at 810, a user response to the query at 820, and/or the type of event detected at 640 (FIG. 6). At 840 the relevant audio feature data is extracted from the most recent audio snippet and subsequently transmitted to the knowledgebase.

The type of features and algorithms for extracting features at 840 and criteria for selecting relevant audio feature data at 830 may evolve over time. For example, the type of features, algorithms for extracting features, and criteria for selecting relevant audio feature data may be defined by the knowledgebase and transmitted to the personal audio system as updates to firmware and/or software for the processors in the active audio filters and/or the personal computing device.

Referring once again to FIG. 6, at 670 the extracted feature data and associated metadata may be transmitted or uploaded to the sound knowledgebase. The uploaded metadata may include a location of the personal audio system provided by a geo-locator within the personal audio system. When the trigger event was a user-initiated or automatic retrieval of a selected processing parameter set from the parameter memory, the uploaded metadata may include an identifier of the selected processing parameter set and/or the actual selected processing parameter set. When the trigger event was the user modifying the active processing parameter or creating a new processing parameter set, the uploaded metadata may include the modified or new processing parameter set. Further, the user may be prompted to enter a context, descriptor, or other tag to be associated with the modified or new processing parameter set and uploaded. The process 600 may then return to 620 and continue cyclically until stopped.

At 710, the sound knowledgebase receives the feature data and associated metadata transmitted at 670 and may receive additional feature data and metadata from other personal audio systems. Analysis may be performed on the received feature data at 720. The audio analysis at 720 may develop additional data about the features and characteristics of the ambient audio at the personal audio system. The additional data developed by the audio analysis at 720 may be stored in a database at 730 in association with the corresponding audio feature data and metadata received at 710.

At 740, machine learning techniques may be applied to learn revised and/or new processing parameter sets from the feature data, additional data, and metadata stored in the database 730. A variety of analytical techniques may be used to learn revised and/or new processing parameter sets. These analytical techniques may include, for example, numerical and statistical analysis of feature data, additional data, and metadata such as location, date, and time metadata. These analytical techniques may include, for further example, semantic analysis of tags, descriptors, contexts, and other non-numerical metadata.

As an example of a learning process that may be performed at 740, some or all of the records in the database at 730 may be sorted into a plurality of clusters based according to feature data, location, tag or descriptor, or some other factor. Some or all of these clusters may optionally be sorted into sub-clusters based on another factor. When records are sorted into clusters or sub-clusters based on non-numerical metadata (e.g. tags or descriptors) semantic analysis may be used to combine like metadata into a manageable number of clusters or sub-clusters. A consensus processing parameter set may then be developed for each cluster or sub-cluster. For example, clear outliers may be discarded and the consensus processing parameter set may be formed from the medians or means of processing parameters within the remaining processing parameter sets.

New or revised processing parameter sets learned and stored at 740 may be transmitted to some or all of the plurality of personal audio systems at 750. For example, new or recently revised processing parameter sets may be pushed to some or all of the personal audio systems on an as-available basis, which is to say as soon as the new or recently revised processing parameter sets are created. Processing parameters sets, including new and revised processing parameter sets may be transmitted to some or all of the personal audio systems at predetermined periodic intervals, such as, for example, nightly, weekly, or at some other interval. Processing parameters sets, including new and revised processing parameter sets may be transmitted upon request from individual personal audio systems. Processing parameter sets may be pushed to, or downloaded by, a personal audio system based on a change in the location of the personal audio system. For example, a personal audio system that relocates to a position near or in an airport may receive one or more processing parameters sets for use suppressing aircraft noise.

FIG. 8 shows flow charts of methods 900 and 1000 for processing sound using collective feedforward akin to those shown in FIG. 6.

The method 900 may start at 905 and run continuously until stopped (not shown). The actions at 910 to 950 are the same as the corresponding actions (610 to 650) in the process 600 as shown in FIG. 6. Descriptions of these actions will not be repeated.

In response to detecting a trigger event (“yes” at 940), a determination may be made at 980 whether or not the most recent audio snippet contains speech. Methods for speech detection are known to those of skill in audio processing. When the most recent audio snippet contains speech (“yes” at 980), audio feature data may be extracted from the most recent audio snippet as previously described. The extracted audio feature data and associated metadata may be transmitted to the sound knowledgebase at 970. When the most recent audio snippet does not contain speech (“no” at 980), the most recent audio snippet and associated metadata to the sound knowledgebase at 990.

The actions 1010 to 1040 of the process 1000 are similar to the corresponding actions within the process 700 shown in FIG. 6, with the exception that the process 1000 receives, stores, processes, and learns from both audio feature data and audio snippets uploaded from personal audio systems.

The overall process of learning new or revised processing parameter sets based on audio snippets and metadata and providing those new or revised processing parameter sets to personal audio systems is referred to herein as “collective feedforward”. The term “collective” indicates the new or revised processing parameter sets are learned from the collective inputs from multiple personal audio systems. The term “feedforward” (in contrast to “feedback”) indicates new or revised processing parameter sets are provided, or fed forward, to personal audio systems that may not have contributed snippets and metadata to the creation of those new or revised processing parameter sets.

Information collected by the sound knowledgebase about how personal audio systems are used in different locations, ambient sound environments, and situations may be useful for more than developing new or revised processing parameter sets. In particular, information received from users of personal audio systems may indicate a degree of satisfaction with an ambient sound environment. For example, information may be collected from personal audio systems at a concert to gauge listener satisfaction with the “house” sound. If all or a large portion of the personal audio systems were used to substantially modify the house sound, a presumption may be made that the audience (those with and without personal audio systems) was not satisfied. Information received from personal audio systems could be used similarly to gauge user satisfaction with the sound and noise levels within stores, restaurants, shopping malls, and the like. Information received from personal audio systems could also be used to create soundscapes or sound level maps that may be helpful, for example, for urban planning and traffic flow engineering.

Closing Comments

Throughout this description, the embodiments and examples shown should be considered as exemplars, rather than limitations on the apparatus and procedures disclosed or claimed. Although many of the examples presented herein involve specific combinations of method acts or system elements, it should be understood that those acts and those elements may be combined in other ways to accomplish the same objectives. With regard to flowcharts, additional and fewer steps may be taken, and the steps as shown may be combined or further refined to achieve the methods described herein. Acts, elements and features discussed only in connection with one embodiment are not intended to be excluded from a similar role in other embodiments.

As used herein, “plurality” means two or more. As used herein, a “set” of items may include one or more of such items. As used herein, whether in the written description or the claims, the terms “comprising”, “including”, “carrying”, “having”, “containing”, “involving”, and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of”, respectively, are closed or semi-closed transitional phrases with respect to claims. Use of ordinal terms such as “first”, “second”, “third”, etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements. As used herein, “and/or” means that the listed items are alternatives, but the alternatives also include any combination of the listed items. 

1. A system, comprising: a processor configured to: analyze audio feature data and associated metadata received from a personal audio system, wherein the audio feature data is extracted from a most recent snippet of an ambient audio stream; develop a new processing parameter set based on the audio feature data; and provide the new processing parameter set to personal audio system and one or more other personal audio systems; and a memory coupled the processor and configured to store the new processing parameter set.
 2. The system of claim 1, wherein the processor is further configured to receive the audio feature data in response to a trigger event associated with the personal audio system.
 3. The system of claim 2, wherein the trigger event includes retrieving a selected processing parameter set from a dataset memory of the personal audio system for use as an active processing parameter set.
 4. The system of claim 2, wherein the trigger event includes receiving, via the user interface, a command to store an active processing parameter set in a dataset memory, and the associated metadata transmitted to the device includes the active processing parameter set.
 5. The system of claim 1, wherein the associated metadata includes the active processing parameter set before modification.
 6. The system of claim 1, wherein the associated metadata transmitted to the device includes a descriptor received from a user via a user interface.
 7. The system of claim 1, wherein the associated metadata transmitted to the device includes data indicating the location of the personal audio system.
 8. The system of claim 1, wherein the audio feature data includes one or more of arithmetic mean, geometric mean, harmonic mean, centroid, variance, standard deviation, spectral skew, kurtosis, spectral shape, rolloff, spread, flatness, spectral flux, Mel frequency cepstral coefficients, pitch, tonal power ratio, harmonic-to-average power ratio, maximum of autocorrelation function, zero crossing rate, RMS power, peak power, crest factor, amplitude/power envelope and attack/decay rates.
 9. The system of claim 1, wherein the processor is configured to develop the new processing parameter set using one or more machine learning techniques.
 10. The system of claim 1, wherein the processor is configured to develop the new processing parameter set using one or more analytical techniques.
 11. The system of claim 1, wherein to develop a new processing parameter set includes: sorting the audio feature data and associated metadata from the personal audio system and additional audio feature data and associated metadata from the other personal audio systems into a plurality of clusters; and developing a corresponding consensus processing parameter set for each cluster of the plurality of clusters.
 12. The system of claim 11, wherein the plurality of clusters are sorted based on the associated metadata from the personal audio system and the associated metadata from the other personal audio systems.
 13. The system of claim 11, wherein the corresponding consensus processor parameter set is based on a median or average of processing parameters associated with a cluster.
 14. The system of claim 11, wherein an outlier processing parameter is discarded from a cluster.
 15. A method, comprising: analyzing audio feature data and associated metadata received from a personal audio system, wherein the audio feature data is extracted from a most recent snippet of an ambient audio stream; developing a new processing parameter set based on the audio feature data; and providing the new processing parameter set to personal audio system and one or more other personal audio systems.
 16. The method of claim 15, further comprising receiving the audio feature data in response to a trigger event associated with the personal audio system.
 17. The method of claim 15, wherein developing a new processing parameter set includes: sorting the audio feature data and associated metadata from the personal audio system and additional audio feature data and associated metadata from the other personal audio systems into a plurality of clusters; and developing a corresponding consensus processing parameter set for each cluster of the plurality of clusters.
 18. The method of claim 17, wherein the plurality of clusters are sorted based on the associated metadata from the personal audio system and the associated metadata from the other personal audio systems
 19. The method of claim 17, wherein the corresponding consensus processor parameter set is based on a median or average of processing parameters associated with a cluster.
 20. A computer program product, the computer program product being embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for: analyzing audio feature data and associated metadata received from a personal audio system, wherein the audio feature data is extracted from a most recent snippet of an ambient audio stream; developing a new processing parameter set based on the audio feature data; and providing the new processing parameter set to personal audio system and one or more other personal audio systems. 